big data

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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Compressing The Analysis-To-Action Life Cycle

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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Overview
Enterprises are making unprecedented investments in big data and analytics. In order to improve
business processes and customer experiences, companies aspire to use big data to build predictive
and/or pattern recognition models. However, as data scientists strive to increase their rate of
experimentation and get their work applied to real-world scenarios, otherwise referred to as
operationalizing big data analytics experiments, they are challenged by the tools and, in particular, the
availability of both external data sources and integration with internal application sources. Leading
enterprises are continuing to look to increase the rate of data experimentation and streamline the process
of using models in production.
In this Oracle-commissioned study, Forrester evaluated data science trends across industries and
identified key challenges that stand in the way of operationalizing big data experiments.
Geography
159 analytics decision-makers
from the US, Canada, the UK,
Brazil, and Australia
Company size
Enterprises with 1,000-plus
employees
Role
Manager-level and above IT
and business decision-makers
with responsibility for analytics
Industry
Respondents span a range
of industries.

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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Companies Value Big Data Insights But Haven’t Mastered Big
Data Innovation
Enterprises have increased their investments in big data and analytics in order to improve their ability
to innovate and make better business decisions. Forrester’s Global Business Technographics® Data And
Analytics Survey, 2016 found that 63% of companies rated improving their ability to innovate as a high or
critical priority, and 56% similarly prioritize leveraging big data and analytics in business decision making.
Most organizations lack maturity in the execution of their big data analytics projects. While 42% listed big
data as critical to improving business outcomes, only 34% feel that they have the right people and 29%
feel they have the culture and processes to support their big data analytics efforts. Additionally, only 28%
strongly agreed that they use big data to experiment or test new ideas.

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Only 19% of organizations
strongly agreed that they are
able to efficiently apply big
data insights to operational
processes in a timely manner.
A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Operationalizing Big Data Insights Is A
Critical Challenge
Data scientists and analysts struggle to get their insights and models
operationalized in production systems. Eighty-three percent of
analytics decision-makers in our study agreed or strongly agreed
that it is very challenging to move data experiments to production.
One of the key contributing challenges is the difficulty in working with
siloed data, tools, and systems that limit an organization’s ability to
integrate data from both internal and external sources. This lack of
integration ultimately limits the speed of experimentation.
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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Challenges For Operationalizing Data Include Disparate Tools,
Applications, And Data
Respondents cited a range of challenges for operationalizing data, ranging from technical (including
insufficient, blunt tools and a lack of integration) to organizational (including a lack of executive buy-in and
a mistrust of analytics and models among business stakeholders).
Tools
› Large number of
independent tools
Applications
› Challenges embedding
analytics in applications
Data
› Challenges accessing
required data
› Siloed data and/or
inconsistent use of data
Business operations
› Lack of executive
sponsorship
› Lack of confidence in models

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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Organizations Actively Seek To Compress Analytics Processes
To Increase Rate Of Innovation And Data-Driven Decision Making
Over two-thirds of companies are investing in or planning to invest in platforms to share out data content,
data innovation capabilities, and predictive systems. Predictive analytics play a key role — 46% of firms
have implemented predictive analytics and another 27% plan to in the next year .

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A Custom Technology Adoption Profile Commissioned By Oracle | January 2017
The Big Data Imperative
Project Director:
Mark Brozek
Sr. Market Impact Consultant
Contributing Research:
Forrester’s Application
Development & Delivery
research group
Conclusion
Big data can be a powerful driver of innovation, but only when an organization has the right systems in
place to effectively analyze data and to operationalize big data insights in production systems.
Unfortunately, most companies suffer from disparate tools and data, which act as barriers to their
analytics efforts. Decision-makers who are seeking the benefits of big data analytics are investing in
integrated analytics platforms and building out processes to facilitate experimentation and sharing.
METHODOLOGY
In this study, Forrester conducted a survey of 159 analytics decision-makers from enterprises in the US,
Canada, the UK, Brazil, and Australia. They were manager-level or above IT and business decision-
makers with responsibility for analytics decision-makers. Respondents were given a small incentive for
their time. This study was supplemented by Forrester’s Business Technographics Global Data And
Analytics Survey, 2016.
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